1. Technical Field
The present invention relates to blind source separation (BSS) and more particularly, to non-square BSS under coherent noise.
2. Discussion of the Related Art
Over the past several years, a variety of BSS techniques have been introduced to separate independent audio signal sources from an array of sensors. The BSS techniques that have been developed sometimes focus on real audio and noisy data. Most techniques, however, focus on the “square” case of source separation (i.e., when there is an equal number of sources and sensors), while some focus on the “non-square” or degenerate case of source separation (i.e., when there is an un-equal number of sources and sensors). With regard to the “non-square” case, claims of generalization have been made; however, these claims have not clearly indicated how they would scale, neither from an algorithmic perspective nor in terms of computational properties.
Certain BSS techniques have used a maximum likelihood (ML) estimator to estimate the mixing parameters of the signal sources. For example, one known technique derived the ML estimator of the mixing parameters in the presence of Gaussian sensor noise. In this technique, however, the noise element represented a technicality in that it was considered in the limit zero in order to be able to determine parameter update equations. In another known technique, the ML estimators were derived from noisy data that did not come from an isotropic noise field.